In this paper, we continue this push towards practicality, albeit with a different perspective – we importantly focus on the understudied problem of unseen (or rarely seen) word recognition, where no (or limited) word image of a particular character sequence is present during training. Our motivation is straightforward – humans can recognise a word image, even when it falls beyond the scope of known vocabulary. In fact, robustness of a text recognition framework largely depends on its performance on rarely or unseen words [1]}. Note that unlike the conventional zero-shot [2]} setting where the transfer happens on class-level, here the combination of characters is “unseen" although the characters individually have come up in training. The fact that the sequences not being encountered during training is what makes this task challenging. Our solution for this “unseen” problem is intuitive: (i) we leverage an iterative framework with a feedback mechanism to give the model a chance to re-visit its false predictions, and (ii) we explicitly ask such feedback to encapsulate useful information that would help the model to correct itself at the following iteration.
